Coursework Sample on Financial Risk Modelling
1. Introduction
1.1 Aim and objective
The aim of the company Blue corn Manufacturing Ltd. has been decided to expand its business to the chain with the least risk retaining investment.
- To evaluate the cash flow condition of the company by using NPV technique.
- To evaluate the possible risk of the company after the expansion of the business.
1.2 Background history
The company Blue corn manufacturing Ltd. has the automobile business and ancillary services and the company has a subsidiary business in China. The chief operating officer is considering the firm’s investments regarding the extension plan in China. The company has faced uncertainties due to the Brexit effect and the Covid-19 pandemic situation. In the new business extension plan.
1.3 Literature review
This study highlights the discounted cash flow method and sensitivity analysis method to measure the financial condition of the company. Discounted cash flow is calculated to measure the future possible outcome from the initial investment (Laitinen, 2019). On the other hand, sensitivity analyses are calculated to assess the possible risk of the company.
2. Methods
This analysis of the company is decided to focus on the weighted average cost of capital, measurement of the net present value, discounted cash flow method, and another process of the risk assessments. In order to assess the future market risks, the risk analyst decided to consider the 95% confidence level and based on that measure the largest NPV value of the company (Gupta and Razavi, 2018). The measurement of the coefficient of the regression, coefficient of the correlation, and variance.
Discounted cash flow method
The methods of the DCF are being measured with the help of calculating the cash flows of the company. The risk analyst is decided to consider the value of discounting rate and based on that calculate the future possible outcome (Tabei et al. 2019). In this aspect, discounted cash flow is different from the net present value as both the methodology are being used at the same discount rate. In this study, the risk analyst decides to calculate the discounted cash flow by analysing the investment decision amount of the new business expansion. Discounted cash flow method exists as a method to figure out the future value or the initial investment.
95% confidence interval
95% confidence interval is the process of figuring out the recovering condition of the project from the assessed risks. It is a range where the risk values are being discussed and the value of the population mean (Stancu et al. 2017). Lower the range of the risk value denoted the lower risk condition of the company. On the other hand, higher the value from the range has been denoted that there are high-risk positions of the company.
Regression coefficient
The regression coefficient denotes the estimated value of the population parameter among the respondents and predictors. The furniture risk can be assumed with the help of the regression coefficient. The results of the regression coefficient help to judge the value and risk achieving expectation of the company. This regression coefficient is the graphical representation of the values.
Correlation coefficient
Correlation coefficients are the specific measurement of the strengths between two variables within the correlation analysis. As per the view of Kim et al. (2017), these methods symbolize the relationship and provide the results based on risk factors. There are various correlation coefficient methods that help to discuss the various risk variables. Such as Pearson correlation coefficient, linear correlation coefficient, and many more.
Variance
Variance is the statistical tool that helps to measure the absolute deviation of a data set. The calculation of the variance is basically ensured by taking the value of the mean, and standard deviation.
3. Results and Discussions
3.1 Discounted cash flow
The uncertain result of the pandemic and Brexit effect on the company has affected the financial condition of the company. In this regard, the company tried to recover the situation and took the investment decision based on the discounted cash flow method. The discount rate is considered 12% in order to calculate the discounting cash flow method. There are some uncertain inputs and parameters of the distribution. As per the view of Carras et al. (2020), these distributions are going to measure the 10 years value of discounted cash flows. As per parameter 1, the discounted cash flow is 929.73 and parameter 2 explains the value of 449207.83. On the other hand, the parameter 3 value is explained as 256816.06.
| Parameter 1 | |||
| Cash flows (£ms) | Cash Flows | DCF @12% | PV (@12%) |
| Year 0 | (90,000.00) | 1.00 | (90,000.00) |
| Year 1 | 8,000.00 | 0.89 | 7,142.86 |
| Year 2 | 10,000.00 | 0.80 | 7,971.94 |
| Year 3 | 12,100.00 | 0.71 | 8,612.54 |
| Year 4 | 14,305.00 | 0.64 | 9,091.09 |
| Year 5 | 16,620.25 | 0.57 | 9,430.78 |
| Year 6 | 19,051.26 | 0.51 | 9,651.96 |
| Year 7 | 21,603.83 | 0.45 | 9,772.47 |
| Year 8 | 24,284.02 | 0.40 | 9,807.91 |
| Year 9 | 27,098.22 | 0.36 | 9,771.89 |
| Year 10 | 30,053.13 | 0.32 | 9,676.30 |
| NPV = | 929.73 | ||
Table 1: Discounted cash flow parameter 1
(Source: Created by the learner)
| Parameter 2 | |||
| Cash flows (£ms) | Cash Flows | DCF @12% | PV (@12%) |
| Year 0 | (100,000.00) | 1.00 | (100,000.00) |
| Year 1 | 63,000.00 | 0.89 | 56,250.00 |
| Year 2 | 70,840.00 | 0.80 | 56,473.21 |
| Year 3 | 79,307.20 | 0.71 | 56,449.30 |
| Year 4 | 88,451.78 | 0.64 | 56,212.70 |
| Year 5 | 98,327.92 | 0.57 | 55,793.90 |
| Year 6 | 108,994.15 | 0.51 | 55,219.83 |
| Year 7 | 120,513.68 | 0.45 | 54,514.27 |
| Year 8 | 132,954.78 | 0.40 | 53,698.21 |
| Year 9 | 146,391.16 | 0.36 | 52,790.12 |
| Year 10 | 160,902.45 | 0.32 | 51,806.28 |
| NPV = | 449,207.83 | ||
Table 2: Discounted cash flow parameter 2
(Source: Created by the learner)
| Parameter 3 | |||
| Cash flows (£ms) | Cash Flows | DCF @12% | PV (@12%) |
| Year 0 | (150,000.00) | 1.00 | (150,000.00) |
| Year 1 | 72,000.00 | 0.89 | 64,285.71 |
| Year 2 | 72,000.00 | 0.80 | 57,397.96 |
| Year 3 | 72,000.00 | 0.71 | 51,248.18 |
| Year 4 | 72,000.00 | 0.64 | 45,757.30 |
| Year 5 | 72,000.00 | 0.57 | 40,854.73 |
| Year 6 | 72,000.00 | 0.51 | 36,477.44 |
| Year 7 | 72,000.00 | 0.45 | 32,569.14 |
| Year 8 | 72,000.00 | 0.40 | 29,079.59 |
| Year 9 | 72,000.00 | 0.36 | 25,963.92 |
| Year 10 | 72,000.00 | 0.32 | 23,182.07 |
| NPV = | 256,816.06 | ||
Table 3: Discounted cash flow parameter 3
(Source: Created by the learner)
3.2 95% confidence interval
The value of the 95% confidence level is being explained by taking the simulation level 10000. The 95% confidence interval is expressed by 1.96 and the range value of the simulation level is 19600. As per the determination of the probability of getting negative NPV output must occur in parameter 1 only (VanderWeele and Ding, 2017). The reason behind this statement is that the interval value in being 19600 and the parameter 1 NPV value is 923.73. In the case of the other parameters, the value of NPV is higher than the interval value, there is no chance to determine the negative value of NPV.
| 95% confidence level | |
| simulation level | 10000 |
| interval | 1.96 |
| value | 19600 |
| para 1 | 929.73 |
| NPV value | -18670.27 |
Table 4: 95% interval
(Source: Created by the learner)
3.3 Sensitivity analysis
Sensitivity analysis defines the variables which affect the business operation and changes the relationship among the variables. Sensitivity analysis requires the output value of the NPV of different parameters. Based on that it has been discussed the change in output, regression coefficient, correlation coefficient, and variance.
- a) The value change in the output NPV is discussed as the change of percentage among the net present value of the parameters. From parameter 1 the change of the output percentage is positive up to year 7 and after that the percentage value is negative (Qian and Mahdi, 2020). On the other hand, in parameter 2 there is only parameter 1 that has the positive value and the rest of the years have a negative value. Similarly, parameter 3 has a negative percentage change in output.
| Change in output | ||
| Year | parameter 1 | |
| 1 | 7142.86 | 11.61% |
| 2 | 7971.94 | 8.04% |
| 3 | 8612.54 | 5.56% |
| 4 | 9091.09 | 3.74% |
| 5 | 9430.78 | 2.35% |
| 6 | 9651.96 | 1.25% |
| 7 | 9772.47 | 0.36% |
| 8 | 9807.91 | -0.37% |
| 9 | 9771.89 | -0.98% |
| 10 | 9676.30 | – |
| Year | parameter 2 | |
| 1 | 56250.00 | 0.40% |
| 2 | 56473.21 | -0.04% |
| 3 | 56449.30 | -0.42% |
| 4 | 56212.70 | -0.75% |
| 5 | 55793.90 | -1.03% |
| 6 | 55219.83 | -1.28% |
| 7 | 54514.27 | -1.50% |
| 8 | 53698.21 | -1.69% |
| 9 | 52790.12 | -1.86% |
| 10 | 51806.28 | – |
| Year | parameter 3 | |
| 1 | 64285.71 | -10.71% |
| 2 | 57397.96 | -10.71% |
| 3 | 51248.18 | -10.71% |
| 4 | 45757.30 | -10.71% |
| 5 | 40854.73 | -10.71% |
| 6 | 36477.44 | -10.71% |
| 7 | 32569.14 | -10.71% |
| 8 | 29079.59 | -10.71% |
| 9 | 25963.92 | -10.71% |
| 10 | 23182.07 | – |
Table 5: Change in output
(Source: Created by the learner)
- b) Regression coefficient value is good in parameter 1 compared to parameters 2 and 3. It is the graphical representation of the data set. In this study, the data set of the net present value expresses that the parameter in the regression coefficient graph is indicating the high possibility of having profitability (Gupta and Razavi, 2018). On the other hand, parameter 2 and 3 indicates the lower value of the regression analysis.
Figure 1: Regression coefficient
(Source: Created by the learner)
Figure 2: Regression coefficient
(Source: Created by the learner)
Figure 3: Regression coefficient
(Source: Created by the learner)
- c) correlation coefficient is measured by using the cash flow method and net present value amount of the parameters. In the data set, the correlation coefficient value of parameter 1 is 0.847 and parameter 2 value is -0.96. The value of parameter 3 is null as there is no difference among the cash flow values between the 10 years.
| correlation coefficient | |
| parameter 1 | 0.84704488 |
| parameter 2 | -0.9674232 |
| parameter 3 | – |
Table 6: Correlation coefficient
(Source: Created by the learner)
- d) The value of variance is calculated to discuss the net present value of the cash flows of the parameters. The variance value of parameter 1 is 832346.78 and for parameter 2 is 2770424.543. On the other hand, the variance value of parameter 3 is 190552571.80. As per the variance value discussion, it has been concluded that parameter 2 is in better financial condition and there are low-risk achievement possibilities.
| Variance of Parameters | |
| Para 1 | 832346.7836 |
| Para 2 | 2770424.543 |
| Para 3 | 190552571.8 |
Table 7: Variance
(Source: Created by the learner)
4. Conclusion
The company decided to malaise the initial condition of the company and measure the future risks that have been going to affect the company. These risks are considered after the new business expansion in China. China has the largest market growth in the industrial business and automobile devices. Hence, in order to examine the new business in the Chain market is going to provide the profitability condition of the company.
4.1 Recommendation
The company has decided to expand the business on the china industry market; hence, it has been analysing the investment decision and future expected risk analysis. In order to analyse the risks company select the three parameters for the uncertain situation (Mukhametzyanov and Pamucar, 2018). Based on that, it has been suggested to the company to invest in parameter 1. As the parameter, 1 has better regression and correlation coefficient value. Apart from this, parameter 1 did not cross the 95% confidence level, so the risk achieving amount is low in parameter 1.
Reference list:
Carras, M.A., Knowler, D., Pearce, C.M., Hamer, A., Chopin, T. and Weaire, T., 2020. A discounted cash-flow analysis of salmon monoculture and Integrated Multi-Trophic Aquaculture in eastern Canada. Aquaculture Economics & Management, 24(1), pp.43-63.
Gupta, H.V. and Razavi, S., 2018. Revisiting the basis of sensitivity analysis for dynamical earth system models. Water Resources Research, 54(11), pp.8692-8717.
Kim, Y., Shin, K., Ahn, J. and Lee, E.B., 2017. Probabilistic cash flow-based optimal investment timing using two-color rainbow options valuation for economic sustainability appraisement. Sustainability, 9(10), p.1781.
Laitinen, E.K., 2019. Discounted Cash Flow (DCF) as a measure of startup financial success.
Mukhametzyanov, I. and Pamucar, D., 2018. A sensitivity analysis in MCDM problems: A statistical approach. Decision making: applications in management and engineering, 1(2), pp.51-80.
Qian, G. and Mahdi, A., 2020. Sensitivity analysis methods in the biomedical sciences. Mathematical biosciences, 323, p.108306.
Stancu, I., Obrejabraşoveanu, L., Ciobanu, A. and Stancu, A.T., 2017. ARE COMPANY VALUATION MODELS THE SAME?-A COMPARATIVE ANALYSIS BETWEEN THE DISCOUNTED CASH FLOWS (DCF), THE ADJUSTED NET ASSET, VALUE AND PRICE MULTIPLES, THE MARKET VALUE ADDED (MVA) AND THE RESIDUAL INCOME (RI) MODELS. Economic Computation & Economic Cybernetics Studies & Research, 51(3).
Tabei, S.M.A., Bagherpour, M. and Mahmoudi, A., 2019. Application of fuzzy modelling to predict construction projects cash flow. Periodica Polytechnica Civil Engineering, 63(2), pp.647-659.
VanderWeele, T.J. and Ding, P., 2017. Sensitivity analysis in observational research: introducing the E-value. Annals of internal medicine, 167(4), pp.268-274.
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